Research on Improved Deep Convolutional Generative Adversarial Networks for Insufficient Samples of Gas Turbine Rotor System Fault Diagnosis

Liu, Shucong; Wang, Hongjun; Zhang, Xiang

doi:10.3390/app12073606

Cited by 2 publications

(2 citation statements)

References 47 publications

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“…Improved GAN models have been proposed in further research, such as ACGAN [35], CGAN [36], WGAN [37], and InfoGAN [38]. These models have also been applied in the fields of image recognition and fault diagnosis [39]. Compared with the other GAN models described above, the DCGAN has rapid convergence and better quality of generator output [40].…”

Section: Gan Modelmentioning

confidence: 99%

Improved and accurate fault diagnostic model for gas turbine based on 2D-wavelet transform and generative adversarial network

Yao

Wang

Song

et al. 2023

Meas. Sci. Technol.

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severe working environment causes gas turbines to break down, which can directly affect their performance. Research on the diagnostic methods for gas turbine faults, such as, gas path faults and sensor failures, has always raised concerns. However, traditional fault diagnosis algorithms mostly use instantaneous data rather than time-series data, because they cannot efficiently use time-series analysis to extract fault features and improve algorithm accuracy. Problems with sparse fault samples and categories are also encountered with these algorithms. In this study, a gas turbine fault diagnostic method based on a 2D-wavelet transform and generative adversarial network (GAN) was proposed. The data preprocessing method, 2D-wavelet transform, of multiple time series (MTS) images was used to obtain fault features. Based on the Fréchet inception distance (FID), a performance evaluation index, an optimal generator built from a deep convolutional generative adversarial network model was selected to solve sparse or imbalanced datasets. The classification accuracy of the four algorithms, namely, random forest, support vector machine, convolutional neural network, and deep neural network, verified the performance of the data preprocessing and dataset building methods mentioned earlier. Compared with the original data, the 2D wavelet transform effectively improved the model accuracy. The generated samples also improved the misclassification issue caused by the imbalanced dataset; however, the ratio of real and generated samples in datasets still requires more attention.

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Section: Gan Modelmentioning

confidence: 99%

Improved and accurate fault diagnostic model for gas turbine based on 2D-wavelet transform and generative adversarial network

Yao

Wang

Song

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…This method can not only separate the shock components in the early fault signal of the gas turbine rotor system, but also effectively suppress the impact of noise on the sparse decomposition process. Interference has a good noise reduction effect, and better preserves high-amplitude amplitude components [36,37].…”

Section: Recognition and Analysis Of Gas Turbine Rotor System Impact ...mentioning

confidence: 99%

Impact Load Sparse Recognition Method Based on Mc Penalty Function

et al. 2022

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The rotor system is an important part of large-scale rotating machinery. Bearings, as a key component of the rotor system, play a vital role in the healthy operation of the rotor system. The bearings operate under harsh conditions such as high temperature, high pressure, and high speed. They are complex and extremely prone to failure, especially when the bearing is affected by impact load, which seriously affects the remaining service life of the bearing. Uneven bearing friction, caused by the impact, is one of the main factors that cause premature failure of the bearing. The early identification of shock loads and reasonable measures are extremely important for the safe operation of equipment. This paper proposes an impact load identification method based on the sparse decomposition of the Mini-max concave penalty function (Mini-max concave penalty function, MC). The method uses the MC penalty function to reconstruct the regularized sparse recognition model, and then uses the improved original dual interior point method to solve the problem. This model realizes the identification of vibration and shock loads. Relevant experimental verification was carried out, and the results show that the sparse decomposition result based on the MC penalty function is better than the L1-regularized sparse decomposition result, and the noise is well suppressed in the non-loaded area of the impact load. This method can be applied to the early fault diagnosis of the vibration signal of the gas turbine rotor.

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Research on Improved Deep Convolutional Generative Adversarial Networks for Insufficient Samples of Gas Turbine Rotor System Fault Diagnosis

Cited by 2 publications

References 47 publications

Improved and accurate fault diagnostic model for gas turbine based on 2D-wavelet transform and generative adversarial network

Improved and accurate fault diagnostic model for gas turbine based on 2D-wavelet transform and generative adversarial network

Impact Load Sparse Recognition Method Based on Mc Penalty Function

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